DESCRIPTION

Scripts and information on the figures and graphs presented in the datapaper Atlantic-Camtraps.

V.0. by Renata Muylaert.

WORKING GROUP

  • Fernando Lima, D.Sc.
  • Renata Muylaert, D.Sc.
  • Milton Ribeiro, D.Sc.

Data prep

Select species and effort data

data <- read.csv(here("data", "splist_filtered_2017_04d24.csv"), sep = ",")
ef <- read.csv(here("data", "effort.txt"), sep="\t")

View(data)

Organize by order and frequency of occurrency - FO

data <- data[order(data$fo),]

str(data)

unique(data$sp_id)

head(data)

FIGURE 01 - MAP

Generated in ArcGis by Fernando Lima.

Fig. 1. Distribution of the camera trap surveys of medium and large terrestrial mammal communities within the Atlantic Forest extent. Gray shows the Atlantic Forest extent with remaining forest patches in green (sensu Ribeiro et. al., 2009). Blue dots show the geographic locations of studies.

FIGURE 02 - SUNBURST

This figure was generated in MS Excel using the sunburst function.

Fig. 2. Taxonomic information levels of medium and large terrestrial mammal species recorded in camera trap surveys within the Atlantic Forest. Only species considered well detected by camera traps are listed. From the 83 species reported in the database, 8 are not listed because the identification is at genera level. Another 28 species are not listed because they were considered opportunistic records of species not usually detected by camera traps (primates, bats, small rodents, and small marsupials).

FIGURE 03 - DISTRIBUTION OF FREQUENCIES OF OCCURRENCE

fct_rev(fct_infreq(factor(data$species.1)))
 [1] Mazama americana          Mazama gouazoubira        Mazama nana              
 [4] Sus scrofa                Pecari tajacu             Tayassu pecari           
 [7] Canis familiaris          Cerdocyon thous           Chrysocyon brachyurus    
[10] Lycalopex gymnocercus     Speothos venaticus        Felis catus              
[13] Leopardus pardalis        Leopardus guttulus        Leopardus wiedii         
[16] Panthera onca             Puma concolor             Puma yagouaroundi        
[19] Conepatus chinga          Conepatus semistriatus    Eira barbara             
[22] Galictis cuja             Lontra longicaudis        Nasua nasua              
[25] Potos flavus              Procyon cancrivorus       Cabassous tatouay        
[28] Cabassous unicinctus      Dasypus novemcinctus      Dasypus septemcinctus    
[31] Euphractus sexcinctus     Priodontes maximus        Didelphis albiventris    
[34] Didelphis aurita          Lepus europaeus           Sylvilagus brasiliensis  
[37] Tapirus terrestris        Myrmecophaga tridactyla   Tamandua tetradactyla    
[40] Hydrochoerus hydrochaeris Cuniculus paca            Dasyprocta azarae        
[43] Dasyprocta leporina       Chaetomys subspinosus     Coendou prehensilis      
[46] Sphiggurus insidiosus     Sphiggurus villosus      
47 Levels: Tayassu pecari Tapirus terrestris ... Cabassous tatouay
colnames(data)[3] <- paste("Order")

p <-  ggplot(data, aes(x=reorder(species.1,-fo), y=fo, fill=Order))+
      geom_bar(
        width=0.8,
        stat="identity",
        color = "black",
        position="stack",
        size =0.3)+
      theme_bw()+
    xlab("Species")+
    ylab("Frequency of occurrence")+
      theme(legend.position="top")+
    theme(axis.text.x = element_text(
              angle=45,
              vjust=1,
              hjust = 1,
              family="serif",
              face="italic",
              colour="black",
              size=rel(0.9)))+
    geom_text(aes(label=iucn_status),
        position=position_dodge(width=0.7),
        size=3,
        family="serif",
        colour="black",
        vjust=-0.25)+
    theme(plot.margin = unit(c(0.5,0.5,0.5,1),"cm"))

cores<-c("indianred1" , # pH # artiodactyla mudar para indianred #cd5c5c rgb(205,92,92)
    "coral",  # pH # carnivora #ff7f50 rgb(255,127,80)
    "orchid" , # pH # cingulata mudar para orchid 
    "burlywood", # pH # didelphimorphia #deb887 rgb(222,184,135)
    "khaki", # pH # lagomorpha mudar para khaki #f0e68c rgb(240,230,140)
    "darkseagreen", # pH #  perisodactyla mudar para darkseagreen #8fbc8f rgb(143,188,143)
    "mediumaquamarine", # pH # pilosa #66cdaa rgb(102,205,170)   
    "dodgerblue" ) # pH #  rodentia mudar para dodger blue #1e90ff rgb(30,144,255)

#png(filename= "Fig3_v19.png", res= 300,  height= 16, width=26, unit="cm")
    #p+ scale_fill_manual(values=cores)
#dev.off()
# pH #  mesmo que acima, mas usando os codigos
#   cores<-c("#cd5c5c", # pH # artiodactyla mudar para indianred #cd5c5c rgb(205,92,92)
#            "#ff7f50",  # pH # carnivora #ff7f50 rgb(255,127,80)
#            "#da70d6", # pH # cingulata mudar para orchid #da70d6 rgb(218,112,214)
#            "#deb887", # pH # didelphimorphia #deb887 rgb(222,184,135)
#            "#f0e68c", # pH # lagomorpha mudar para khaki #f0e68c rgb(240,230,140)
#            "#8fbc8f", # pH #  perisodactyla mudar para darkseagreen #8fbc8f rgb(143,188,143)
#            "#66cdaa", # pH # pilosa #66cdaa rgb(102,205,170)   
#            "#1e90ff") # pH # rodentia mudar para dodger blue #1e90ff rgb(30,144,255)
    
    #png(filename= "Fig3_v20.png", res= 300,  height= 16, width=26, unit="cm")
    #p+ scale_fill_manual(values=cores)
#dev.off()

Fig. 3. Distribution of frequencies of occurrence of the main species evaluated in ATLANTIC-CAMTRAPS, and their status in the 2017 IUCN Red list of threatened species. LC = least concern, NT = near threatened, VU = vulnerable, EN = endangered, CR = critically endangered, DD = data deficient, and IN = invasive species (not an IUCN category).

FIGURE 04 - DISTRIBUTION OF TAXONOMIC RICHNESS AND SAMPLING EFFORT

Generated in ArcGis by Fernando Lima.

Fig. 4. Distribution of taxonomic richness and sampling effort across Atlantic Forest sites where camera traps were used for sampling of medium and large terrestrial mammal species. Opportunistic records (see the text) were removed from this analysis.

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